Abstract
As malignant transformation requires synchronization of growth-driving signaling (S) and metabolic (M) pathways, defining cancer-specific S-M interconnected networks (SMINs) could lead to better understanding of oncogenic processes. In a systems-biology approach, we developed a mathematical model for SMINs in mutated EGF receptor (EGFRvIII) compared to wild-type EGF receptor (EGFRwt) expressing glioblastoma multiforme (GBM). Starting with experimentally validated human protein-protein interactome data for S-M pathways, and incorporating proteomic data for EGFRvIII and EGFRwt GBM cells and patient transcriptomic data, we designed a dynamic model for EGFR-driven GBM-specific information flow. Key nodes and paths identified by in silico perturbation were validated experimentally when inhibition of signaling pathway proteins altered expression of metabolic proteins as predicted by the model. This demonstrated capacity of the model to identify unknown connections between signaling and metabolic pathways, explain the robustness of oncogenic SMINs, predict drug escape, and assist identification of drug targets and the development of combination therapies.
Highlights
Diseases like cancer involve a large range of components that interact via complex and highly dynamic networks [1,2,3], and are interconnected with biochemical pathways [4,5,6,7]
Complex and highly dynamic interconnected networks allow cancer to take different routes and circumvent chemotherapy. Understanding these context-specific networks and their dynamics of molecular interactions driven by different oncogenic signaling and metabolic pathways is very much needed to predict drug targets and the effect of therapeutics
Construction of a signaling to metabolic pathway interconnection network As a starting model, we constructed an integrated network where signaling (S) and metabolic (M) pathway proteins were connected through protein-protein interactors (PPIs)
Summary
Diseases like cancer involve a large range of components that interact via complex and highly dynamic networks [1,2,3], and are interconnected with biochemical pathways [4,5,6,7]. These multipath interconnections may allow cancer and other diseases to take alternate routes and bypass the effects of therapeutic interventions. Systems biology approaches may predict combination therapies for cancers driven by different oncogenic signaling and metabolic pathways
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